U.S. patent application number 16/758228 was filed with the patent office on 2020-09-10 for system and method for navigating within a track network.
The applicant listed for this patent is PLASSER & THEURER EXPORT VON BAHNBAUMASCHINEN GMBH. Invention is credited to KRZYSZTOF WILCZEK.
Application Number | 20200283040 16/758228 |
Document ID | / |
Family ID | 1000004895853 |
Filed Date | 2020-09-10 |
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United States Patent
Application |
20200283040 |
Kind Code |
A1 |
WILCZEK; KRZYSZTOF |
September 10, 2020 |
SYSTEM AND METHOD FOR NAVIGATING WITHIN A TRACK NETWORK
Abstract
A system for navigating within a track network includes, as
system components, a system central, a track maintenance machine
and a communication device. The system central is set up for
administering network data representing a model of the track
network. The track maintenance machine is suited for treatment of
track sections of the track network. The track maintenance machine
includes a navigation device for processing navigation data derived
from the network data. The communication device is provided for
data exchange between the system central and the navigation device.
The system includes at least one movable or stationary carrier
platform with sensors for collecting raw data representing
characteristic information of the track network. A big data
framework is set up in the system central to evaluate the raw data
and synchronize them with the network data. Automated updating of
the network data can be carried out with the system.
Inventors: |
WILCZEK; KRZYSZTOF; (WIEN,
AT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
PLASSER & THEURER EXPORT VON BAHNBAUMASCHINEN GMBH |
WIEN |
|
AT |
|
|
Family ID: |
1000004895853 |
Appl. No.: |
16/758228 |
Filed: |
October 9, 2018 |
PCT Filed: |
October 9, 2018 |
PCT NO: |
PCT/EP2018/077423 |
371 Date: |
April 22, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B61L 27/0083 20130101;
B61L 27/0005 20130101; B61L 27/04 20130101; B61L 27/0077
20130101 |
International
Class: |
B61L 27/00 20060101
B61L027/00; B61L 27/04 20060101 B61L027/04 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 9, 2017 |
AT |
A 437/2017 |
Claims
1-15. (canceled)
16. A system for navigating within a track network, the system
comprising: a system central for administering network data
representing a model of the track network; a track maintenance
machine for treating track sections of the track network, said
track maintenance machine including a navigation device for
processing navigation data derived from the network data; a
communication device for data exchange between said system central
and said navigation device; at least one movable or stationary
carrier platform having sensors for collecting raw data
representing characteristic information of the track network; and a
big data framework disposed in said system central for evaluating
the raw data and synchronizing the raw data with the network
data.
17. The system according to claim 16, wherein said track
maintenance machine is constructed as a carrier platform including
a sensor system having said sensors collecting the raw data during
travel on the track network.
18. The system according to claim 16, which further comprises a
carrier platform constructed as a measuring vehicle or a
track-bound vehicle equipped with sensors.
19. The system according to claim 16, which further comprises a
flying carrier platform or a drone equipped with sensors.
20. The system according to claim 16, wherein the network data are
stored as a graph with track objects as nodes having relationships
between said track objects as edges.
21. The system according to claim 20, which further comprises
characteristic data patterns specified for one of said track
objects.
22. A method of operating a system for navigating within a track
network, the method comprising the following steps: providing a
system according to claim 16; using said sensors to collect the raw
data; transmitting the raw data to said system central; generating
object data from the raw data by using identification algorithms;
and synchronizing the network data with the object data to update
the network data.
23. The method according to claim 22, which further comprises,
after performing an update of the network data, transferring all of
the updated data or part of the updated data to said navigation
device of said track maintenance machine.
24. The method according to claim 22, which further comprises
assigning probability values or probability functions to the
respective object data in dependence on at least one of said
sensors or a carrier platform or identification algorithms.
25. The method according to claim 24, which further comprises
carrying out an update of the network data by new object data in
dependence on the assigned probability values or probability
functions.
26. The method according to claim 22, which further comprises
organizing the object data based on a detected motion pattern of a
carrier platform, to supply track objects represented by the object
data being strung together as an object chain for synchronization
with the network data stored as a graph.
27. The method according to claim 26, which further comprises
subdividing the object chain into segments, and synchronizing a
segment with the graph based on distinctive track objects.
28. The method according to claim 27, which further comprises
synchronizing a segment with a partial graph by specifying an
extent of agreement, and replacing the partial graph with the
segment when an extent of agreement exceeds a pre-set minimum
extent.
29. The method according to claim 27, which further comprises
synchronizing a segment with a partial graph, and maintaining a
non-verifiable track object as a node of the partial graph until a
pre-set number of failed verifications has been reached.
30. The method according to claim 22, which further comprises using
the sensors disposed on the track maintenance machine to record
surrounding track objects, and determining a current position of
the track maintenance machine by synchronization the recorded track
objects with the network data.
Description
FIELD OF TECHNOLOGY
[0001] The invention relates to a system for navigating within a
track network, comprising as system components a system central, a
track maintenance machine and communication means. The system
central is set up for administering network data which represent a
model of the track network. The track maintenance machine is suited
for the treatment of track sections of the track network, wherein
the track maintenance machine comprises a navigation device for
processing navigation data derived from the network data. The
communication means are provided for data exchange between the
system central and the navigation device. In addition, the
invention relates to a method of operating the system.
PRIOR ART
[0002] Navigation within a track network is required for the
operation of track maintenance machines. Initially, operational
plans including operating locations and work orders are compiled
manually or in a partially automatized way in a system central.
Then, a transfer of operating plan lists to the track maintenance
machine takes place in order to specify the defined operating
locations as navigation targets. As a rule during this, it is
assumed that a machine operator knows the local circumstances.
[0003] According to EP 1 862 593 A2, a system is known which
enables an automatized localization of a track maintenance machine
within a track network by means of a satellite-based tracking
system. In this, synchronization with characteristic data of a
track line database takes place in order to make a precise position
determination.
[0004] In the course of construction site planning, it may happen
that an operating location is not clearly defined or is indicated
incorrectly. For example, an invalid reference may be noted in the
operating plans. Such mistakes can lead to a delay in work
execution. In the worst case, an operation takes place on a wrong
track section.
[0005] According to the prior art, operating plans are compiled on
the basis of network data which are stored in several different
databases. In this, the databases in each case concern different
installations within the track network (for example, signalling
equipment, track, catenaries, etc.). These databases are tailored
to the respective requirements of the various planning authorities
of a railway infrastructure operator, wherein, as a rule, different
references (for example, mileage, signs for masts, sleepers,
signalling device etc.) are stored to indicate an operating
location.
[0006] If the track network is changed, such as, for example, when
a switch is removed, the network data in the known databases have
to be updated manually. In practice, there are frequently problems
with the consistency, completeness, validity and precision of the
available network data. In further sequence, the deficient data
maintenance leads to faulty operation planning of the track
maintenance machine.
SUMMARY OF THE INVENTION
[0007] It is the object of the invention to provide an improvement
over the prior art for a system and for a method of the type
mentioned at the beginning.
[0008] According to the invention, these objects are achieved by
way of the features of independent claims 1 and 7. Advantageous
further developments of the invention become apparent from the
dependent claims.
[0009] In this, it is provided that the system comprises at least
one movable or stationary carrier platform with sensors for
collecting raw data which represent characteristic information of
the track network, and that a big data framework is set up in the
system central in order to evaluate the raw data and synchronize
them with the network data.
[0010] In this manner, the network data are continuously adjusted
to a detected actual state. In this, the setup of the big data
framework in the system central enables an automatized evaluation
of the collected raw data on the basis of pre-set evaluation
criteria or -algorithms. During this, not only conventional
databases and data analysis tools are used, but also various
processes of machine learning. In this manner, a model of the track
network is constructed on the basis of the collected raw data and
continuously further developed. Based on the model and a sensor
system installed on the track maintenance machine, an automatized
real-time position determination in takes place. In addition, work
parameters can be pre-defined independent of location.
[0011] In this, it is advantageous if the track maintenance machine
is designed as a carrier platform and comprises a sensor system
which collects raw data during travel on the track network. Thus,
the network data become ever more precise solely by increasing
operational use of the track maintenance machine, so that accurate
operational planning is ensured.
[0012] In addition, it is useful if the system comprises as carrier
platform a measuring vehicle or other track-bound vehicle equipped
with sensors. As a rule, a track network is traveled over at
prescribed time intervals by a measuring vehicle in order to
establish an actual condition. In case of integration into the
present system, the raw data collected by means of the measuring
vehicle also serve for updating the network data. Conventional rail
vehicles may also be equipped with sensor systems for this
purpose.
[0013] A further improvement provides that the system comprises a
flying carrier platform, in particular a drone equipped with
sensors. Also, high-resolution satellite images can be used as raw
data in order to augment the network data with information.
[0014] In an advantageous embodiment of the invention, the network
data are stored as a graph with track objects as nodes and with
relations between the track objects as edges. In this way, the
network data can be administered in a simple manner. In particular,
the synchronization with the evaluated raw data is facilitated by
suitable algorithms.
[0015] In this, it is favourable if characteristic data patterns
are specified for a track object. Basis for this are significant
features common to every object in a certain class (for example,
sleepers, rail fastening means, light signals, etc.). As a
consequence, depending on the sensors used, corresponding data
patterns are found in the raw data which ensure an efficient object
allocation.
[0016] The method, according to the invention, for operating one of
the afore-mentioned systems provides that the raw data are
collected by means of the sensors, that the raw data are
transmitted to the system central, that object data are generated
from the raw data by means of identification algorithms, and that
the network data are synchronized with the object data in order to
update the network data.
[0017] In this, the big data framework set up in the system central
serves for automatic evaluation of the collected raw data. With
this method, an automatized adaptation of the network data takes
place as soon as corresponding raw data are collected by means of
the sensors. Thus, problems based on deficient data maintenance are
precluded.
[0018] In a favourable further development of the method, after an
update of the network data has taken place, all the updated data or
part of the updated data are transferred to the navigation device
of the track maintenance machine. If the system encompasses several
track maintenance machines, a corresponding data transfer takes
place to all machines. In this way, the navigation to the next
operating location is always based on updated network data.
[0019] An advantageous embodiment of the method provides that
probability values or probability functions are assigned to the
object data in each case in dependence on the sensors used and/or
the carrier platform used and/or the identification algorithms
used. In this manner, an identification precision in relation to
the data stock or the track network is ascertained. Thus, a
classification of the determined objects takes place with respect
to their information content for the track network or for the
already existing track network information.
[0020] The machine learning in the scope of the big data framework
enables the continuous expansion and adaptation of the recognizable
objects on the basis of new raw data. For example, definitions or
algorithms deposited in an object register are updated with new
data.
[0021] In this, it is favourable if an update of the network data
by new object data is carried out in dependence on the assigned
probability values or probability functions. The network data are
thus brought up-to-date in that new object data are added on the
basis of the evaluated information content.
[0022] A further improvement provides that the object data are
organized on the basis of a detected motion pattern of the carrier
platform, so that track objects represented by the object data,
strung together as an object chain, are supplied for the
synchronization with the network data stored as a graph. This
simplifies the synchronisation because, due to the detected motion
pattern, a logical sequencing of several track objects takes place.
Thus, the significance of an object chain with complex structure is
derived from the significance of the individual track objects.
[0023] Advantageously in this, the object chain is subdivided into
segments, wherein a segment is synchronized with the graph on the
basis of distinctive track objects. This method step likewise
optimizes the synchronization of the network data with the object
data.
[0024] A simple to carry out synchronization of a segment with a
partial graph provides that an extent of agreement is specified,
and that the partial graph is replaced by the segment if the extent
of agreement exceeds a pre-set minimum extent.
[0025] The fault tolerance of the method is heightened if, when
synchronizing a segment to a partial graph, a non-verifiable track
object remains as node of the partial graph until a pre-set number
of failed verifications has been reached. In this manner, sensor-
or transmission defects have no influence on the quality of the
network data.
[0026] For a navigation procedure, it is advantageous if
surrounding track objects are recorded by means of sensors arranged
on the track maintenance machine, and if a current position of the
track maintenance machine is determined by synchronization of the
recorded track objects with the network data. In this, changes of
the track network are automatically taken into account during
navigation of the track maintenance machine.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] The invention will be described below by way of example with
reference to the accompanying drawings. There is shown in a
schematic manner in:
[0028] FIG. 1 track maintenance machine
[0029] FIG. 2 track network
[0030] FIG. 3 system layout
[0031] FIG. 4 structure of track sections
[0032] FIG. 5 method sequence
DESCRIPTION OF THE EMBODIMENTS
[0033] A track maintenance machine 1 shown in FIG. 1, which is to
be navigated to a work assignment, is a component of the system
according to the invention. This track maintenance machine 1
comprises working units 2 for treatment of a track section 3 of a
track network 4. The track network 4 includes various objects such
as, for example, tracks 5, switches 6, crossings 7, masts 8,
tunnels 9, stations 10, underpasses 11, level crossings 12 or
balises 13, as shown by example in FIG. 2.
[0034] The track maintenance machine 1 is further equipped with
various sensors or sensor systems 14 in order to record the
surroundings of the track 5 traveled upon, and the current
position. These are, for example, a camera 15, a positioning system
16, a clearance gauge scanner 17 or a rail scanner 18. In this
manner, the track maintenance machine 1 functions as a carrier
platform for the sensors or sensor systems 14.
[0035] In order to get to a track section 3 to be worked on, the
track maintenance machine 1 has a navigation device 19. This is
configured as a computing- and controlling unit and serves for
navigation within the track network 4 which is represented by
network data. By means of the navigation device 19, navigation data
derived from the network data are processed and synchronized with
sensor data in order to determine the current position of the track
maintenance machine 1.
[0036] One object of the present invention is to continuously
update the network data in an automatized way. To that end, at raw
data representing characteristic information of the track network 4
are first collected by means of the sensors or sensor systems 14.
In further sequence, the raw data are evaluated and synchronized
with the network data administered in a system central 20. From
this data synchronization it is possible to draw conclusions with
regard to the condition of the track network 4 or individual track
sections 3. For example, a frequent change of collected position
data allows conclusions as to an unstable track position.
[0037] Besides the track maintenance machine 1, other carrier
platforms can be used for collecting the raw data, for example a
measuring vehicle 21, another rail vehicle 22 or a flying carrier
platform 23. Infrastructure facilities equipped with sensors 14 can
be used as a stationary carrier platform 24. This might be, for
example, a mast 8 with a camera 15 fastened to it which observes a
track section 3. Also, fibre optic cables installed adjacent to the
track 5 can be used as sensors 14. The basis for this is the
so-called Distributed Acoustic Sensing (DAS) in which laser
impulses are sent through fibre optic cables in order to thus
register in real time along a track section 3 sound signals and
activities which can be derived therefrom. By means of such
stationary carrier platforms 24, raw data of an observed track
section 3 are collected over time. Aside from detecting object
changes, these raw data can also be used for position verification
of moving carrier platforms 1, 21, 22, 23.
[0038] The measuring vehicle 21 is equipped, for example, with a
GNSS receiver 25, a clearance gauge scanner 17 and a rail scanner
18. The other rail vehicle 22 includes a GNSS receiver 25, and the
flying carrier platform 23 comprises a camera 15 or other devices
for recording aerial views. By means of all these sensors or sensor
systems 14, various raw data are collected and supplied for
evaluation. In this, depending on data volume and available
computing power, the raw data are either pre-processed on the
carrier platform 1, 21, 22, 23, 24 or transmitted directly to the
system central 20.
[0039] As can be seen in the illustrative system layout in FIG. 3,
communication means 26 are provided as additional system
components. These communication means 26 serve for data exchange
between the system central 20, the sensor systems 14 and the
navigation device 19. These means are, for example, devices for
wireless communication via a mobile radio network. In a very simple
embodiment of the system according to the invention, the system
central 20 is accommodated in the track maintenance machine 1, so
that there is a self-sufficient system for navigation and network
data updating. In this, the communication means 26 may be elements
of an installed bus system.
[0040] In the system central 20, a big data network 27 is
installed. This also supports various machine learning algorithms
besides conventional databases 28 and data analysis tools. Examples
for this are noSQL or Hadoop. In this manner, the system central 20
serves for collecting, storing and processing the data.
[0041] From data points m.sub.r,s.sup.i(t), the sensor systems 14
generated a data tensor S.sub.k(t) with an arbitrary dimension k at
a point in time t:
S.sub.k(t)=(M.sub.1(t), . . . ,M.sub.k(t)) [0042] wherein i=1, . .
. , k are M.sub.ir.times.s--are matrixes, therefore
[0042] M i ( t ) = ( m 1 , 1 i ( t ) m 1 , s i ( t ) m r , 1 i ( t
) m r , s i ( t ) ) ##EQU00001##
[0043] Sensors 14 arranged on moving carrier platforms 1, 21, 22,
23 deliver in particular data points m.sub.r,s.sup.i(t) with
spatial information. In stationary carrier platforms 24, however,
the data points recorded by means of sensors 14 display especially
temporal changes.
[0044] From characteristic features in the data points
m.sub.r,s.sup.i(t) of the sensor systems 14, virtually indexed
objects 29 (objekt.sub.i) are compiled as object data. These
represent track objects 4-13 which are recognizable with stochastic
reliability in the track environment and can serve as reference for
navigation. Specifically, the objects 29 are characterized by
significantly reproducible patterns. In an object register, the
definitions (or algorithms) of the objects 29 are updated by new
data.
[0045] During this, a probability is calculated for each object
characteristic or a probability function P.sub.x is assigned:
objekt v i ( t ) = ( objekt id P id time t P t coordinates x , y ,
z P x , y , z offset .DELTA. x , .DELTA. y , .DELTA. z P .DELTA. x
, .DELTA. y , .DELTA. z value .DELTA. t P .DELTA. t next objekt O i
+ 1 P O i + 1 .DELTA. t O i + 1 P .DELTA. t O i + 1 data points M 1
P M 1 data points M x P M x ) ##EQU00002##
[0046] Aside from the metadata resulting from the object
verification, the data points in particular describe a current
state of the virtually indexed object 29.
[0047] The respective probability function P is dependent on the
type of sensor or sensor system 14, the type of carrier platform 1,
21, 22, 23, 24 and the algorithms in the object register. For
example, a probability function with little scattering is
prescribed for newer sensor systems 14. For older sensors 14 with
less precision, however, a greater scattering is prescribed. A
recognition probability derived from stored observation processes
takes into account the number of objects 29 recognized so far. In
this way, a degree of unambiguousness of an object 29 in relation
to a data stock stored in the system central 20 is established.
Accordingly, a classification of the objects 29 by means of the
evaluated information content takes place with respect to the total
track network and the information therein.
[0048] In addition, the evaluation method is continuously improved
in that the recognizable objects 29 are continuously expanded and
adapted by means of machine learning or manual learning. In this,
the autonomous machine learning within the big data framework is
based on those data which are newly collected in the system central
20.
[0049] As shown in FIG. 4, virtually indexed objects 29 are linked
relative to one another according to a motion pattern of the
carrier platform 1, 21, 22. Thus, in the case of track-bound
carrier platforms 1, 21, 22 strung-together virtually indexed
objects 29 are generated, wherein the resulting object chain 30 can
be regarded as a semantic chain K.sub.t.sub.n:
K.sub.t.sub.n=(objekt.sub.vi(t.sub.n), . . .
,objekt.sub.vi(t.sub.n+m)),
wherein t.sub.n is a starting time and t.sub.n+m is an ending time
of a run of the carrier platform 1, 21, 22.
[0050] Within this chain, distinctive objects 29 (for example,
object data of a switch 6) are specified as discretization points
for subdivision into segments 31. These can be determined
dynamically, from the probability of recognition or from the
relevancy in the network (degree of unambiguousness). In FIG. 4 for
example, the strung-together objects 29 represent, from left to
right, a switch 6, a balise 13, a track 5, a mast 8, a tunnel
portal, a tunnel 9, a tunnel portal, a switch 6, a mast 8, a balise
13, two masts 8, and a switch 6.
[0051] New object chains 30 are synchronized with the network data,
i.e. the model 31 of the track network 4. In this, the network data
are stored as a graph N(t.sub.a), wherein t.sub.a is a time of
updating of the particular object 29:
N(t.sub.a)=(objekt.sub.v(t),edge.sub.v(t))
N(t.sub.new)=objekt.sub.vi(t).fwdarw.N(t.sub.old)
[0052] Segments 32 of the object chain 30 are synchronized with the
model 31 of the track network 4 (mapped) via distinctive objects
29. If a segment 29 and a partial graph coincide with a high
probability, then the virtually indexed objects 29 contained in the
segment 29 are transferred to the graph. In this manner, the
characteristics of the particular virtually indexed object 29 are
used for updating the characteristics of the model 31 (update of
the network data) while taking into account the associated
probability functions P. In this, with growing collection of raw
data, the reliability and precision of the network data
increases:
N ( t a ) = ( ( objekt id P id update t a P t a coordinates x , y ,
z P x , y , z value d P d data points M 1 P M 1 data points M x P M
x ) ( next objekt O v + 1 P O .nu. + 1 distance next objekt x , y ,
z P x , y , z ) ) ##EQU00003##
[0053] Data points collected by means of stationary carrier
platforms 24 are also synchronized in a corresponding manner with
the network data, wherein here the information about temporal
changes is paramount.
[0054] During an updating process, it may happen that, based on
sensor faults or obstructions during object detection, individual
objects 29 cannot be verified. Then it is practical if these remain
existent in the network data until--during a new collecting
procedure 33--a falsification takes place, or a verification fails
to happen several times.
[0055] Shown FIG. 5 is the method sequence in an overview. In this,
the method is divided roughly into the following method steps:
[0056] input 34 of the various carrier platforms 1, 21, 22, 23, 24
and the sensor systems 14 [0057] signal analysis 35 for object
indexing with statistical values [0058] repeated collecting
procedures 33 (observations) for verification or falsification of
the object indexing [0059] mapping 36 of various collection types
or observation perspectives [0060] output 37 of the track network
model 31 (updated network data) [0061] navigation procedure 38 of a
track maintenance machine 1
[0062] In an initial phase, the system forms an initial model 31
for the track network 4 on the basis of the pre-set algorithms. To
that end, for example, aerial views, raw data of a measuring
vehicle 21, raw data of the track maintenance machine 1 and raw
data of another vehicle 22 are evaluated. In FIG. 5, the respective
signal analysis 35 initially results in different objects 29 or
object classes which can be associated with individual track
objects 5-13. In aerial views, these are, for example, tracks 5,
switches 6, tunnels 9, masts 8, stations 10, etc. The raw data of
the track-bound carrier platforms 1, 22, 23 can be associated with
the track objects tracks 5, switches 6, tunnels 9, etc.
[0063] In the course of the repeated collecting procedures 33, the
individual objects 29 are verified or falsified with regard to
their relation to one another. In the case of the track-bound
carrier platforms 1, 22, 23, object chains 30 are formed which
depict a track section 3 traveled upon. A merging of these
evaluation results by means of mapping 36 results at last in the
model 31 of the recorded track network 4.
[0064] For reliably carrying out a navigation procedure 38, the
network data are transmitted from the system central 20 to the
navigation device 19 of the track maintenance machine 1 after a
data update has taken place or at prescribed time intervals. In
this, it is favourable if, via the desired confidence, it is
parameterized which objects 29 contained in the network data are
transmitted for navigation to the track maintenance machine 1.
[0065] During the navigation procedure 38 itself, objects or object
data 29 are synchronized with those track objects 5-13 which are
currently detected in the surroundings of the track maintenance
machine 1 by means of the sensors or sensor systems 14. In this
way, detected track objects 5-13 serve as reference for position
determination. Additionally, results of a track gauge measurement
can be used for more precise positioning on the track 5. In this,
the detected progression of the track gauge forms further data
points of those objects 29 which represent the corresponding track
5. In an extension, it is also possible to use detectable
characteristic features of sleepers 39 or rails 40 (markings,
material characteristics, etc.).
[0066] Additionally, the raw data collected during the navigation
process 38 serve as new data input for updating the network data.
With the present system, changes in the track network 4 due to
reconstruction or malfunctions are automatically taken into account
for subsequent navigation procedures 38.
[0067] Cognition takes place cause- or time-related automatically
in dependence on the speed of migration of the track network 4.
[0068] Favourably, recognized and anticipated objects 29 in the
current surroundings of the track maintenance machine 1 are
displayed to a machine operator 41 for orientation. Work
instructions can additionally be included in this display. Also,
work parameters can be prescribed to the machine operator 41 in a
location-dependent way or transmitted directly to a working unit 2.
In this way, an automatized local adaptation of work parameters
takes place, thus enabling an optimized operation of the track
maintenance machine 1. In a tamping machine, these are, for
example, lifting- and lining values as well as time stipulations
for the tamping cycles. In other track maintenance machines 1, work
parameters like ballast demand, spoil quantities of old ballast can
be adjusted dependent on location.
* * * * *